Awesome LeetCode resources to learn Data Structures and Algorithms and prepare for Coding Interviews.
This report summarizes scanner evidence for ashishps1/awesome-leetcode-resources, a Java project with 17,987 GitHub stars. SynthScan examined 3,056 lines of code across 57 source files, recording 29 pattern matches distributed across 4 syntactic categories. The legacy adjusted pattern signal is 18.3 (Elevated pattern signal); it is not an authorship estimate.
Use the findings to ask whether maintainers can explain intent, verify dependencies, simplify unnecessary scaffolding, and focus review effort. Risk Payload v1 adds ownership, supply-chain, over-engineering, and review-effort vectors when available. These are prompts for human verification, not probabilities, security findings, or measured maintenance costs.
Use each vector to guide manual verification of the underlying findings.
Can maintainers explain intent, assumptions, and trade-offs? 29 finding(s).
Are imports declared and package/API names verified? 0 finding(s).
Are wrappers, guards, and repeated structures necessary? 10 finding(s).
Which changes need focused human review? 29 finding(s).
This chart tracks the legacy adjusted pattern score across scan runs. Changes can reflect source changes, scan coverage, or detector/scoring versions; they do not measure AI adoption or prove that a team refactored code. Compare runs only when their scoring versions are compatible. The dashed line tracks the raw pattern-hit count.
Groups findings by configured severity weight. Severity is not a confidence estimate, security rating, or proof that a package is missing. Verify imports against the project's manifests and inspect each finding in its local design context.
This horizontal bar chart shows where configured pattern scores accumulate across directories. Treat the bars as locations to inspect, not as evidence of authorship or code quality. Use the file-level findings to decide whether a directory needs a focused ownership, dependency, complexity, or review check.
The scanner identified 29 distinct pattern matches across 4 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a detector match to inspect in context. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.
Reading the findings table: The Severity column indicates the configured finding weight (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — legacy comment-context matches receive a ×1.5 score weight. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier under the legacy scoring model.
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW⚡ | patterns/java/TopKElements.java | 13 | // Step 1: Sort the array in descending order | COMMENT |
| LOW⚡ | patterns/java/TopKElements.java | 17 | // Step 2: Extract the first K elements | COMMENT |
| LOW⚡ | patterns/java/TopKElements.java | 72 | // Step 1: Build the frequency map | COMMENT |
| LOW⚡ | patterns/java/TopKElements.java | 78 | // Step 2: Sort the entries by frequency in descending order | COMMENT |
| LOW⚡ | patterns/java/TopKElements.java | 82 | // Step 3: Extract the top K elements | COMMENT |
| LOW⚡ | patterns/java/TopKElements.java | 92 | // Step 1: Build the frequency map | COMMENT |
| LOW⚡ | patterns/java/TopKElements.java | 98 | // Step 2: Use a min heap to keep track of the top K elements | COMMENT |
| LOW⚡ | patterns/java/TopKElements.java | 103 | // Step 3: Add each entry to the heap, and maintain size K | COMMENT |
| LOW⚡ | patterns/java/TopKElements.java | 111 | // Step 4: Extract the elements from the heap | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW⚡ | patterns/java/TopKElements.java | 13 | // Step 1: Sort the array in descending order | COMMENT |
| LOW⚡ | patterns/java/TopKElements.java | 17 | // Step 2: Extract the first K elements | COMMENT |
| LOW⚡ | patterns/java/TopKElements.java | 72 | // Step 1: Build the frequency map | COMMENT |
| LOW⚡ | patterns/java/TopKElements.java | 78 | // Step 2: Sort the entries by frequency in descending order | COMMENT |
| LOW⚡ | patterns/java/TopKElements.java | 82 | // Step 3: Extract the top K elements | COMMENT |
| LOW⚡ | patterns/java/TopKElements.java | 92 | // Step 1: Build the frequency map | COMMENT |
| LOW⚡ | patterns/java/TopKElements.java | 98 | // Step 2: Use a min heap to keep track of the top K elements | COMMENT |
| LOW⚡ | patterns/java/TopKElements.java | 103 | // Step 3: Add each entry to the heap, and maintain size K | COMMENT |
| LOW⚡ | patterns/java/TopKElements.java | 111 | // Step 4: Extract the elements from the heap | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW⚡ | patterns/python/top_k_elements.py | 7 | def k_largest_elements_sorting_approach(self, nums, k): | CODE |
| LOW⚡ | patterns/python/top_k_elements.py | 11 | def k_largest_elements_max_heap_approach(self, nums, k): | CODE |
| LOW⚡ | patterns/python/top_k_elements.py | 15 | def k_largest_elements_min_heap_approach(self, nums, k): | CODE |
| LOW⚡ | patterns/python/top_k_elements.py | 25 | def top_k_frequent_elements_sorting_approach(self, nums, k): | CODE |
| LOW⚡ | patterns/python/top_k_elements.py | 30 | def top_k_frequent_elements_min_heap_approach(self, nums, k): | CODE |
| LOW | patterns/python/top_k_elements.py | 43 | def k_closest_points_to_origin_max_heap_approach(self, points, k): | CODE |
| LOW⚡ | patterns/python/sliding_window.py | 2 | def find_max_average_brute_force(self, nums, k): | CODE |
| LOW⚡ | patterns/python/sliding_window.py | 10 | def find_max_average_sliding_window(self, nums, k): | CODE |
| LOW⚡ | patterns/python/sliding_window.py | 20 | def length_of_longest_substring_sliding_window(self, s): | CODE |
| LOW | patterns/python/sliding_window.py | 33 | def length_of_longest_substring_sliding_window_frequency_array(self, s): | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | patterns/go/level_order_traversal.go | 34 | // Example usage | COMMENT |